About the book Following the hierarchical nature of forest resource management, this research focuses on the monitoring and assessment of forest cover at various abstraction levels based upon categorical land use/land cover (LU/LC) classification and change detection as well as empirical estimation of changes at local operational levels. An approach of object-based image analysis (OBIA) based on optical sensor data has been adapted and applied in the destabilized Blue Nile region of Sudan in order to gather the required spatial information in support of future forest planning and decision making. At the categorical level rules have been developed and optimal features have been extracted for each segment. Based on thematic LU/LC maps series of optimised algorithms have been created to depict the dynamics of change of LU/LC entities. Detailed change classes as well as change statistics have been produced. Moreover hot-spot areas have been investigated and aggregated to the community-level. The study utilised a well-designed questionnaire to address the factors affecting LU/LC dynamics and the possible solutions based on the perception of local communities. At the operational structural forest stand level correlation and regression analyses have been applied to identify the relations between a wide range of spectral and textural metrics and the field-derived forest attributes. Furthermore the best fitting models have been cross-validated with an independent set of field samples, which revealed a high degree of precision. The research concludes that OBIA shows a significant capability to serve as an efficient approach to gaining accurate knowledge about land-related features, whether at the level of operational structural forest attributes or at the level of categorical LU/LC classification and change detection. Moreover, the developed methodological framework exhibits a potential solution to attain precise facts and figures about the dynamics of change and their driving forces.